2021 / 04

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DOI: 10.24075/brsmu.2021.040

ORIGINAL RESEARCH

Labelling of data on fundus color pictures used to train a deep learning model enhances its macular pathology recognition capabilities

Takhchidi KhP1, Gliznitsa PV2, Svetozarskiy SN3, Bursov AI4, Shusterzon KA5
About authors

1 Pirogov Russian National Research Medical University, Moscow, Russia

2 OOO Innovatsioonniye Tekhnologii (Innovative Technologies, LLC), Nizhny Novgorod, Russia

3 Volga District Medical Center under the Federal Medical-Biological Agency, Nizhny Novgorod, Russia

4 Ivannikov Institute for System Programming of RAS, Moscow, Russia

5 L.A. Melentiev Energy Systems Institute, Irkutsk, Russia

Correspondence should be addressed: Pavel V. Gliznitsa
Belinskogo, 58/60, et. 5, 603000, Nizhny Novgorod; moc.duolci@pastinzilg

About paper

Funding: this work was financially supported by the Foundation for Assistance to Small Innovative Enterprises in Science and Technology (contract №150ГС1ЦТНТИС5/64226 dated December 22, 2020)

Author contribution: Takhchidi HP — manuscript editing; Gliznitsa PV — study concept and design, data collection and processing, results analysis, manuscript writing; Svetozarskiy SN — participation in data collection, literature and results analysis, manuscript writing; Bursov AI — literature analysis, algorithms development, manuscript editing; Shusterzon KA — algorithms development and validation, illustrations preparation, text writing.

Received: 2021-07-27 Accepted: 2021-08-15 Published online: 2021-08-28
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Fig. 1. Stages of image analysis by Faster RCNN
Fig. 2. Class activation heatmap visualization example, fundus photograph of an AMD patient
Fig. 3. Results of detection of regions of interest and classification of images from the test dataset by Faster RCNN with ResNet50 for convolution. Images correctly identified by the model as healthy retina photographs have green boxes, those with AMD detected have red boxes
Table 1. Clinical classification of AMD [8]
Table 2. Developed models' performance indicators reflecting the quality of detection of AMD in color fundus photographs